Observational studyMedicine2026
Analysis of sex-specific stroke risk factors in middle-aged and elderly Chinese population based on machine learning approach: A retrospective observational cohort study.
Observational study in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Stroke is a leading cause of global mortality and disability, yet comprehensive sex-specific stroke predictors are lacking. This study aimed to identify sex-specific stroke risk factors in middle-aged and elderly Chinese to improve early detection. 12,975 participants (7079 females, 5896 males) aged ≥45 from the China Health and Retirement Longitudinal Study 2011 to 2020 were analyzed. Sex-stratified correlations of 27 health indicators were examined. Eight machine learning algorithms identified significant stroke risk factors. Sex-specific associations between these risk factors and stroke risk were further analyzed using Cox proportional hazards models. Finally, a nomogram was developed to predict stroke risk. Males had higher stroke prevalence than females (P < .001). Nine key predictors were identified: triglyceride and glucose index, waist circumference, low-density lipoprotein-cholesterol, hematocrit, diastolic blood pressure, total metabolic output, mean-corpuscular volume, systolic blood pressure (SBP), waist-to-height ratio, and Cystatin C, with sex differences. High DBP, systolic blood pressure, total metabolic output, and Cystatin C in both males and females were still significantly associated with the risk of stroke (P < .05). The nomogram model exhibited better discrimination compared with other individual predictive factors. This study screened out 9 key parameters through machine learning algorithms and established a nomogram prediction model. It emphasized the importance of simultaneously considering the comprehensive risk score and sex-specific factors in clinical practice, thus providing a scientific basis for improving the prevention and treatment strategies for stroke in middle-aged and elderly populations.
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